Find Academic Supervisors

Luca PezzarossaView profile

Associate Professor

• Real-Time Systems
• Digital Microfluidics
• Compiler Optimizations
• +7 more

Luca Pezzarossa is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on real-time systems, embedded computer systems, digital microfluidics, compiler optimizations, and hardware accelerators. He leads projects such as the Edu4Chip: Joint Education for Advanced Chip Design in Europe initiative and supervises PhD students in areas like compiler optimizations for neural networks and speech enhancement algorithms. His academic journey includes contributions to interdisciplinary fields, combining computer engineering with biomedical applications such as biochip design and PCR optimization. He actively engages in open-source tool development, particularly using the Chisel framework for hardware design education and research. Key research themes include: Real-time systems and time-predictable architectures Compiler-driven optimizations for constrained devices Digital microfluidics for lab-on-a-chip systems Edge computing and TinyML applications Recent publications highlight innovations in microplastic detection on edge devices, dynamic channel pruning for speech enhancement, and parallel execution engines for digital microfluidics. His work aligns with sustainable development goals through environmental applications and energy-efficient technologies. Current projects involve: PhD Supervision: Andrea Cerioli (Compiler Optimizations), Riccardo Miccini (AI-to-Neural Network Mapping), Ehsan Khodadad (Time-predictable Systems) Research Grants: EU-funded Edu4Chip (2023–2025), multiple industry-academia collaborations Labs and teams: Leads the Embedded Systems Engineering group at DTU, focusing on interdisciplinary hardware-software co-design for real-world applications. Active in developing open-source frameworks for education and research.

• Artificial Intelligence
• Machine Learning
• Medical Diagnosis
• +9 more

Henning Christiansen is a Professor at Roskilde University's Department of People and Technology, affiliated with the Programming, Logic and Intelligent Systems (PLIS) research group. He is also a Knight/Chevalier of the Dannebrog (2018) and serves as Coordinator for International Student Exchanges in Computer Science, Informatics & Humanities-Technology Studies. His research spans Deep Learning for medical diagnosis, Robotics in theatrical performances, Constraint Logic Programming, Probabilistic-logic models, Natural Language Processing, Logical methods for context comprehension, Interactive art installations, Database query systems, Cultural technology projects like Viskbook. Key projects include SEAFACTS (digital maritime history platform), EXPLAIN-ME (explainable AI in medical education), NDH (cross-border health data collaboration). Publications highlight contributions to AI ethics, robot choreography, medical image analysis, constraint-based formal methods. He has supervised over 16 projects and 285+ activities, including international conferences and exhibitions. His photography has been displayed in Roskilde libraries and cultural venues.

Xiufeng LiuView profile

Researcher

• Energy Systems
• Data Analytics
• Renewable Energy
• +5 more

Xiufeng Liu is a Senior Researcher at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on smart meter data analytics, big data, and energy systems, contributing to UN Sustainable Development Goals related to affordable and clean energy. He holds a PhD from Aalborg University (2012) and has held positions at IBM Canada, the University of Waterloo, and Åbo Akademi University. Dr. Liu’s expertise spans energy economics, climate policy modeling, and data-driven methodologies. His work integrates machine learning with energy systems, addressing challenges such as wind power forecasting, solar cell optimization, and heat load prediction. Key projects include OPTIX (optimizing positive-energy districts) and ANSWER (wind-solar energy prediction models). His research outputs emphasize sustainable energy solutions, including federated learning frameworks for privacy-preserving data sharing and anomaly detection in energy grids. Collaborations span global institutions, reflecting his commitment to interdisciplinary energy solutions. Dr. Liu has supervised numerous projects and contributed to 148 publications. His work bridges technical innovation with policy implications, aiming to advance climate-compatible energy strategies globally.

• Resource-Aware Machine Learning
• Data Processing on Resource-Constrained Hardware
• Storage Systems
• +5 more

Pınar Tözün is an Associate Professor at the IT University of Copenhagen (ITU) since 2018, where she leads the Data, Systems, and Robotics section and the Resource-Aware Data Systems (RAD) team. Her research focuses on optimizing modern hardware (processors and storage) for data-intensive systems, with recent emphasis on resource-aware machine learning, hardware-constrained data processing, and advancements in SSD/CXL technologies. Before ITU, she was a research staff member at IBM Almaden Research Center (2015–2018), contributing to the IBM Db2 Event Store development. She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL, 2014), where she worked on the Shore-MT storage manager under Prof. Anastasia Ailamaki. She earned her bachelor’s degree in Computer Engineering from Koç University (Turkey, 2009), advised by Prof. Serdar Taşıran. Her research interests span Resource-aware machine learning, Data management systems, Edge computing, High-performance storage, and sustainable AI practices. Her work bridges hardware and software optimization, addressing challenges in distributed systems and IoT environments. She actively contributes to academic communities through workshops like DBTest and diversity initiatives in database conferences.

Peter Schneider-KampView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +5 more

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.

• Offshore Robotics
• Marine Engineering
• Carbon Capture and Storage
• +3 more

Simon Pedersen is an Associate Professor and Head of the Esbjerg Energy Section at Aalborg University's Faculty of Engineering and Science. His research focuses on offshore robotics, marine engineering, carbon capture technologies, and autonomous underwater systems. He leads projects like NextGen Robotics for offshore wind farm maintenance and CarbonAdapt for CO2 storage infrastructure adaptation. Pedersen holds a PhD in Control Engineering from AAU (2016), an M.Sc. in Intelligent Reliable Systems (2013), and a B.Sc. in Electronics and Computer Engineering (2011). Research Interests: Offshore drones, underwater vehicle navigation, robotics control, carbon capture and storage (CCS), and sustainable energy systems. His work addresses UN SDGs related to clean energy and marine environmental protection. Key Projects: Automatic replantation of large-scale eelgrass (2025–2028) NextGen Robotics for underwater inspection (2023–2025) CarbonAdapt: CO2 storage infrastructure adaptation (2022–2025) Publications: Over 80 papers in journals like Journal of Cleaner Production and Carbon Capture Science & Technology , focusing on robotics, CO2 transportation, and energy systems. Recent work includes frameworks for CO2 impurity monitoring and slip prevention in underwater robotics. Awards: Won the Innovation Project of the Year (2024) for contributions to robotics and subsea infrastructure. Advising: Supervises PhD candidates including Kenneth Simonsen (carbon storage) and Esben Uth (autonomous drones). Active in grants totaling millions from Innovation Fund Denmark. Labs/Teams: Leads the Esbjerg Energy Section’s robotics and marine systems teams, collaborating on projects like offshore crawler robots and subsea inspection systems.

Fredrik Fogh SørensenView profile

Assistant Professor

• Marine Growth
• Underwater Robotics
• Remotely Operated Vehicles
• +4 more

Fredrik Fogh Sørensen is an Assistant Professor at Aalborg University's Faculty of Engineering and Science, specializing in the Esbjerg Energy Section. His work focuses on marine and maritime research, particularly in the areas of underwater robotics and offshore energy systems through AAU BLUE – Marine & Maritime Research. Dr. Sørensen's research interests span Marine Growth, Underwater Robotics, Remotely Operated Vehicles, Localization Systems, Offshore Energy, Control Systems, and Sustainable Energy. His work contributes to UN Sustainable Development Goals related to energy sustainability with research fingerprint showing strong connections to Marine Growth (100%), Remotely Operated Vehicle Engineering (92%), and Offshore Structure Engineering (28%). His publication record demonstrates consistent growth from 2 publications in 2019 to 4 in 2024 with 2 more scheduled for 2025. His research shows a clear progression from foundational work to practical applications in offshore environments with particular emphasis on underwater vehicle technology, localization systems, and control mechanisms for ROVs. Dr. Sørensen has received several prestigious awards for his work: Innovation Project of the Year (2024) Best Session Paper Award (2022) Universitetspris 2022 (2022) He is actively involved in significant research projects including ACOMAR (Auto Compact Marine Growth Remover), ACOMAR II, and DIN-ECO (Boosting Digital Innovation and Transformation Capacity of HEIs in an Entrepreneurial ecosystem). His collaborative network spans multiple institutions with strong connections in marine technology research. Dr. Sørensen was featured in Ugeavisen Esbjerg in February 2023 regarding the Esbjerg University Prize 2022, where he was honored as a civil engineer in Sustainable Energy Technology from AAU Esbjerg.

Petar DurdevicView profile

Associate Professor

• Reinforcement Learning
• Deep Learning
• Wastewater Treatment
• +3 more

Petar Durdevic is an Associate Professor at Aalborg University's Faculty of Engineering and Science, within the Energy Section. His research focuses on Reinforcement Learning, Deep Learning, Robotics, and Environmental Engineering, particularly in wastewater treatment optimization and offshore energy systems. He leads projects like OPTIMIZE (Deep Reinforcement Learning for microbial fermentation) and AITEKS (AI-driven tech support). Durdevic collaborates with the Offshore Drones and Robotics and AI for the People teams, addressing challenges in energy technology and sustainable systems. His research interests include predictive control for wastewater treatment, autonomous robotics for pipeline inspection, and AI applications in environmental monitoring. Notable contributions include developing grey-box models for N2O emissions and amphibious quad-rotor systems for infrastructure inspection. Durdevic has supervised one PhD student, Lars D. Hansen, and secured grants from the Novo Nordisk Foundation and Innovation Fund Denmark. Key achievements include advancing fiber rope technology for wind turbines and publishing extensively on topics like deep reinforcement learning, synthetic data generation, and environmental control systems. His work bridges robotics, AI, and environmental science, with applications in offshore energy and sustainable wastewater management. PhD Supervision: Lars D. Hansen (Deep Learning Model Predictive Control for N2O Emission Reduction) Labs/Teams: Offshore Drones and Robotics, AI for the People, BLUE – Marine & Maritime Research Grants: OPTIMIZE (Novo Nordisk Foundation, 2024–2028), AITEKS (Innovation Fund Denmark, 2024–2025)

Henrik Skov MidtibyView profile

Associate Professor

• Robotics
• Computer Vision
• Precision Agriculture
• +4 more

Henrik Skov Midtiby is an Associate Professor at the University of Southern Denmark's Maersk Mc-Kinney Moller Institute and affiliated with the SDU Climate Cluster and SDU UAS Center. His research focuses on robotics, computer vision, precision agriculture, and marine biology, with applications in UAV technology, environmental monitoring, and autonomous systems. He has led projects such as PotBot (autonomous plant transportation) and LEARNING (climate change interactions), and contributed to studies on harbor porpoise behavior and weed management algorithms. Education: PhD from the Maersk Mc-Kinney Moller Institute. Extensive teaching experience in robotics and computer vision courses for unmanned aerial systems (UAS). Research Interests: Unmanned Aerial Vehicles (UAVs), environmental sensing, machine learning for agricultural automation, marine mammal tracking, and energy-efficient robotic systems. Key projects include drone-based crop monitoring, autonomous navigation algorithms, and bioacoustic studies of marine life. Grants & Collaborations: Project manager for Præcisionsfrøavl (precision seedling) and PotBot. Collaborates with institutions on climate change impacts and marine conservation. Active in workshops like 'Teaching for Active Learning.' Labs/Teams: SDU UAS Center for drone research; SDU Climate Cluster for environmental studies. Involves students in projects combining robotics, AI, and ecological applications.

Stephanie ForrestView profile

Professor

• Computational Immunology
• Computer Security
• Automated Software Repair
• +12 more

Stephanie Forrest is a Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where she also serves as Director of the Biodesign Center for Biocomputation, Security and Society. She is an External Faculty member of the Santa Fe Institute (SFI), where she previously held leadership roles including Co-Chair of the Science Board (2010–2013) and Interim Vice President for Academic Affairs (1999–2000). Prior to joining ASU, she was a Regents Distinguished Professor and Department Chair of Computer Science at the University of New Mexico (2006–2011). B.A., St. John's College M.S., Ph.D., University of Michigan, Computer Science Her research lies at the intersection of biology and computation, focusing on computational immunology, automated software repair, evolutionary computation, and modeling complex systems such as cancer and immune responses. She pioneers bio-inspired algorithms for cybersecurity and develops evolutionary techniques for software optimization and repair. Her work integrates principles from immunology, genetics, and complex adaptive systems to solve problems in computer science. The trends in her recent publications reveal a sustained focus on automated program repair using evolutionary computation, GPU code optimization, and modeling biological systems. Her work increasingly combines machine learning with mutation-based search, explores semantic representations in code repair, and applies bio-inspired models to real-world software and security challenges. ACM/AAAI Allen Newell Award (2011) Presidential Young Investigator Award (1991) Stanislaw Ulam Memorial Lectures, SFI (2013) IEEE Fellow UNM Annual Research Lecture (2012) IEEE S&P Test of Time Award (2020) ICSE Most Influential Paper Award (2019) ACM/SIGEVO Impact Award (2019) SEAMS Best Paper Award (2019) WEIS Best Paper Award (2015) Stephanie Forrest has advised numerous students and researchers, often in collaboration with W. Weimer, C. Le Goues, and M. Moses. Her research is supported by major funding agencies including the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory (AFRL), and the Santa Fe Institute. She has also contributed to public policy as a Jefferson Science Fellow at the U.S. Department of State (2013–2014), advising on cyber-policy. She currently chairs the Government Affairs Committee of the Computing Research Association (CRA). She leads the Biodesign Center for Biocomputation, Security and Society, which brings together interdisciplinary teams to study the co-evolution of technology and society. Her research group has developed tools for intrusion detection (e.g., STIDE, pH, RISE), automated software repair (e.g., GenProg), and biological modeling (e.g., CancerSim). Current projects include engineering diversity for enhanced cybersecurity, measuring internet censorship, and modeling immune and cancer systems.

Claus MelvadView profile

Professor

• Mechatronics
• Robotics
• Metrology
• +6 more

Claus Melvad is a Professor at the Department of Mechanical and Production Engineering, School of Engineering, Aarhus University. His primary research focuses on developing robotic systems and metrology equipment for extreme environments, including Arctic and rainforest regions. Expertise: Robotics, Metrology, Autonomous Vehicles, Sensor Systems Affiliation: Aarhus University School of Engineering Research Highlights: Melvad specializes in creating rugged, low-cost instruments for environmental monitoring. His work includes: Autonomous drones for biodiversity and disease monitoring in tropical regions Robotic ocean profilers for polar environments Modular autonomous surface vehicles (ASVs) for climate research Custom sensor systems for extreme condition calibration Publication Trends: Recent projects emphasize drone technology for remote biodiversity monitoring, low-cost Arctic instrumentation, and autonomous sampling systems. Keywords include environmental robotics, sensor development, and extreme environment engineering. Projects: Lead initiatives like GlacierPro (autonomous methane profiler), DISCO (student CubeSat program), and NORDACC (autonomous surface vehicle).

Ruyu LiuView profile

Research Fellow

• Computer Science
• Machine Learning
• Artificial Intelligence
• +3 more

Ruyu Liu is a Research Fellow at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). Their work bridges interdisciplinary research in Energy Economics and Climate Policy with cutting-edge advancements in Computer Science and Artificial Intelligence . Research Interests : Knowledge Distillation Semantic Segmentation Point Cloud Upsampling Neural Network Optimization Machine Learning Applications Climate-Energy Economics Recent Publications highlight trends in AI and computer vision, including: Development of novel knowledge distillation methods to address teacher bias in neural networks. Advancements in semi-supervised semantic segmentation using dual-branch architectures and self-matching strategies. Efficient algorithms for point cloud upsampling leveraging implicit neural representations and spatial hashing. Application of singular value decomposition (SVD) for feature extraction in distillation frameworks.

Muhammad Rizwan AsifView profile

Assistant Professor

• Image Processing
• Computer Vision
• Machine Learning
• +9 more

Muhammad Rizwan Asif is an Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University (AU Engineering). His research focuses on signal processing, machine learning, and deep learning applied to remote sensing and geophysical data analysis. Research Interests include image processing, computer vision, and leveraging deep learning for environmental monitoring and resource management. His work addresses challenges in noise removal, transient electromagnetic data inversion, and feature extraction in satellite imagery. Current projects: Ethio-Nature (2025–2030), Digitalization of plant-based food samples (2024–2024), and Neural Network AEM Pre-processing (2024–2025). Past projects: SOLGRAS (2024–2025), MapField – Nitrate retention (2020–2022), Flood and Drought – surface NMR (2019–2023), and SiTEM – African groundwater (2019–2023). His expertise bridges engineering and environmental science, with a focus on automated data processing and optimization using scientific machine learning.

Nicolaj Haarhøj MalleView profile

Assistant Professor

• Aerial Robotics
• Unmanned Aerial Vehicles (UAVs)
• Power-line Maintenance and Inspection
• +7 more

Nicolaj Haarhøj Malle is an Assistant Professor at the University of Southern Denmark’s Institute of Mechanical and Electrical Engineering and a member of the SDU Digital and High-Frequency Electronics group. He earned his PhD in Robotics in November 2023 with a dissertation on autonomous power-line perception and continues as a post-doctoral researcher until July 2025. Education PhD in Robotics, University of Southern Denmark (2020–2023) Post-doctoral Researcher in Aerial Robotics for Power-line Maintenance, University of Southern Denmark (2023–2025) Research Interests Malle’s work lies at the intersection of aerial robotics, power-line engineering, and embedded intelligence. He develops fully autonomous drones capable of landing on live overhead cables to recharge, inspect, and maintain critical infrastructure. His research integrates mm-wave radar sensing , computer vision , FPGA-accelerated perception , and machine learning to achieve robust navigation in GPS-denied, high-voltage environments. He also pioneers open-source hardware-software architectures for rapid prototyping of safety-critical drone swarms. Scientific Awards & Recognition Although no formal awards are listed, Malle’s work has received extensive national press coverage, including features in Information , DR Forskerfesten PhD Cup , and international tech media highlighting his “vampire drone” concept. Grants & Projects EU H2020 Drones4Safety (2020-2023) – PhD student participant EU H2020 Aerial Core (2019-2023) – PhD student participant InnoExplorer AIR-Ops (2023-2024) – Project participant Spin-outs Denmark OnGrid Aerial Systems (2024-2025) – Project participant Laboratories & Collaborations Malle collaborates closely with the SDU UAS Center and conducts experimental flights at dedicated drone test sites. He maintains an international network via visiting research stays (University of Zürich, Oct–Dec 2022) and active IEEE membership.

Ali SahafiView profile

Assistant Professor

• Electrical Engineering
• Computer Science
• Biomedical Engineering
• +4 more

Ali Sahafi is an Assistant Professor at the Institute of Mechanical and Electrical Engineering , University of Southern Denmark, specializing in Digital and High Frequency Electronics . His research bridges Electrical Engineering , Computer Science , and Biomedical Engineering . Research Interests: Sahafi focuses on neural network accelerators , medical imaging for cardiovascular and gastrointestinal diagnostics, and low-power electronics for embedded systems. His work includes applying YOLO-V8 for polyp detection in colonoscopy and coronary segmentation in angiography, alongside FPGA-based solutions for wireless capsule endoscopy. Project Involvement: He contributed to the EU H2020 TeamPlay project (2018–2020), optimizing time, energy, and security in cyber-physical systems. His lab, Digital and High Frequency Electronics , drives innovations in high-frequency circuits and embedded AI.